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Record W1987373038 · doi:10.1108/10662240210422512

Supporting the e‐business readiness of small and medium‐sized enterprises: approaches and metrics

2002· article· en· W1987373038 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternet Research · 2002
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsDalhousie UniversitySaint Mary's University
Fundersnot available
KeywordsBusinessGovernment (linguistics)Balance (ability)Small and medium-sized enterprisesConceptual frameworkMarketingKnowledge managementProcess managementIndustrial organizationComputer scienceFinanceSociology

Abstract

fetched live from OpenAlex

Government initiatives are continuously being designed to create stable and supportive environments for developing new industries. Presents a conceptual model for use by governments in creating and sustaining an appropriate climate that facilitates the national adoption of e‐business. It focuses specifically on the needs of small and medium‐sized enterprises (SMEs). Also suggests six categories of e‐business readiness metrics and measures to be used for assessing how a country is performing in terms of providing a positive e‐business readiness climate. Examples of innovative initiatives are provided from Canada, The Netherlands, Norway, and Singapore. Concludes that a balance among attention to infrastructure components has not yet been achieved in these countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.454
GPT teacher head0.462
Teacher spread0.008 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it